yellow_tripdata-jupysql-duckdb.ipynb

yellow_tripdata-jupysql-duckdb.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/matsuu/d89288586c6375f69f03843269ae0bf3/larger-than-memory-plotting-with-jupysql-and-duckdb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVfkRDGdmSOB",
        "outputId": "bbdcf20c-328f-4dd6-f0c8-59d50cdf9866"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('yellow_tripdata_2024-07.parquet',\n",
              " <http.client.HTTPMessage at 0x7d77cdbdffd0>)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# Let's grab an example file: a subset of the NYC Taxi dataset:\n",
        "from urllib.request import urlretrieve\n",
        "\n",
        "urlretrieve(\"https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2024-07.parquet\",\n",
        "            \"yellow_tripdata_2024-07.parquet\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install jupysql and the DuckDB SQLAlchemy library duckdb-engine\n",
        "%pip install jupysql duckdb-engine --quiet\n",
        "# Load jupysql\n",
        "%load_ext sql\n",
        "# Connect jupysql to an in memory DuckDB instance\n",
        "%sql duckdb://"
      ],
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        "id": "gD_wTcdxn4sr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd983c1a-25be-453d-b776-c384f91c94e8"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The sql extension is already loaded. To reload it, use:\n",
            "  %reload_ext sql\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%sql\n",
        "CREATE OR REPLACE TABLE yellow_tripdata AS SELECT * FROM 'yellow_tripdata_2024-07.parquet' WHERE trip_distance <= 20.0;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90,
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        "outputId": "25dc2840-4441-4596-f4b4-2c85f6276ddf"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running query in 'duckdb://'"
            ],
            "text/html": [
              "<span style=\"None\">Running query in &#x27;duckdb://&#x27;</span>"
            ]
          },
          "metadata": {}
        },
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              "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))"
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            "text/plain": [
              "+-------+\n",
              "| Count |\n",
              "+-------+\n",
              "+-------+"
            ],
            "text/html": [
              "<table>\n",
              "    <thead>\n",
              "        <tr>\n",
              "            <th>Count</th>\n",
              "        </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "    </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%sqlplot histogram --table 'yellow_tripdata' --column trip_distance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
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        "outputId": "ca84cd29-96d2-4897-f0c7-9468c94775ef"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': \"'trip_distance' from 'yellow_tripdata'\"}, xlabel='trip_distance', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
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fb97PycnRv//9bzVr1qzIUZiidOzYUZL09ttvO0y//A21KCdPniw0KlIQZgo+AqxcubIkFdrnglGWy5d3ZrvF6dKli9zc3DR27NhC/xMv2E5cXJz8/f31//7f/yvyPLDjx4+XefvOeuihh7Rp0yalpaWZ086cOaP3339fkZGRV/z48lrq1auXdu/erRdffFHu7u7q3r27w/wnn3xSv/76qz744INCy547d67Eb6teqkWLFgoJCdGMGTMcPi5eunSp9uzZU+jbp23bttXKlSu1adMmM1Q1a9ZMVapU0fjx4+Xj46OoqKjS7K58fX0lFX5ultXUqVPNfxuGoalTp8rDw0Pt27eX9Mdxv3jxosNlDfLz8/XOO+84rKc0r43i9qGodWRnZ2vmzJml3CtURIxUAf8rKipK06dP19///nfdfvvtCgkJKfQ/dWfVrVtXffr00ebNmxUaGqoPP/xQmZmZpfrD2axZM/Xo0UPvvvuusrOz1aZNG6WmpurHH38scdlZs2bp3Xff1aOPPqo6dero1KlT+uCDD+Tv76+HHnpI0h8foTRs2FBz5sxR3bp1FRQUpEaNGqlRo0a69957NWHCBF24cEG33nqrvvrqKx04cKBMj4X0xzlmr7zyil5//XW1bdtWjz32mLy8vLR582aFh4dr3Lhx8vf31/Tp09WrVy81b95c3bt3V3BwsA4dOqTFixfr7rvvdnhzvBZGjBihTz75RB07dtTgwYMVFBSkWbNm6cCBA/rss88sPS+oNOLj41W1alXNmzdPHTt2VEhIiMP8Xr16ae7cuerfv79WrVqlu+++W/n5+dq7d6/mzp1rXo/LGR4eHnrjjTfUu3dvtWvXTj169DAvqRAZGamhQ4c61Ldt21Yff/yxbDab+XGgu7u72rRpo+XLl+u+++6Tp6dnqfa3IIQNHjxYcXFxRQZJZ3l7e2vZsmVKSEhQdHS0li5dqsWLF+tvf/ubebJ7p06ddPfdd2vEiBE6ePCgeV25y89v8vf3d/q1UbAPr7zyirp37y4PDw916tRJHTp0kKenpzp16qR+/frp9OnT+uCDDxQSEqKjR4+WaR9RgZTX1w6BiiYjI8OIj483qlSpYkgyL69QcLmCoi5PUNwlFeLj443ly5cbTZo0Mby8vIz69esb8+bNK3VP586dMwYPHmxUrVrV8PX1NTp16mQcPny4xEsqbN261ejRo4dRs2ZNw8vLywgJCTEefvhh49tvv3VY/4YNG4yoqCjD09PTYZ2//PKL8eijjxqBgYFGQECA8cQTTxhHjhwptN3iLllQ1ONiGIbx4YcfGnfddZfh5eVl3HLLLUa7du2MlJQUh5pVq1YZcXFxRkBAgOHt7W3UqVPHSExMLNS7M650SYX4+Pgil9m/f7/x+OOPG4GBgYa3t7fRqlUrY9GiRYV6lFTomBb3XCmuj9J4/vnnDUnG7Nmzi5yfl5dnvPHGG8add95pPr5RUVHGmDFjjOzsbLOupEsqFJgzZ455rIKCgoyePXsav/zyS6Ht7tq1y5BkNGjQwGH63//+d0OS8dprr5V6Xy9evGgMGjTICA4ONmw2m3l5hYLLJkycOLHQMsVdUsHX19fYv3+/0aFDB6Ny5cpGaGioMWrUqEKX9jhx4oTRq1cvw9/f3wgICDB69eplbNu2rdA6nX1tGIZhvP7668att95quLm5ObwevvzyS6NJkyaGt7e3ERkZabzxxhvGhx9+WOwlGOA6bIZRhjMnARQrMjJSjRo10qJFi8q7FdxAhg4dqn/961/KyMgwP7rFlSUmJuq///2vTp8+Xd6t4CbBOVUAUMGdP39eH330kbp27UqgAiowzqkCykFJFwD18fExr3mEm9exY8e0YsUK/fe//9WJEyf0wgsvlHdLVyU7O7vEH6529oscQEVEqALKQfXq1a84PyEhweGHYXFz2r17t3r27KmQkBC9/fbbxV6OwlW88MILmjVr1hVrOCMFroxzqoByUPDzGMUJDw8vt6/vA9fK7t27deTIkSvWlNc1ygArEKoAAAAswInqAAAAFuCcquvIbrfryJEjqlKliqU/wwAAAK4dwzB06tQphYeHX/EiwISq6+jIkSPX7IdNAQDAtXX48GHVqFGj2PmEquuoSpUqkv44KP7+/uXcDQAAcEZOTo4iIiLM9/HiEKquo4KP/Pz9/QlVAAC4mJJO3eFEdQAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALBApfJuABVL5IjFTtUdHB9/jTsBAMC1MFIFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFqhU3g3ANUWOWFxizcHx8dehEwAAKgZGqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwQIUJVePHj5fNZtOQIUPMaefPn1dSUpKqVq0qPz8/de3aVZmZmQ7LHTp0SPHx8apcubJCQkL04osv6uLFiw41q1evVvPmzeXl5aXbb79dycnJhbY/bdo0RUZGytvbW9HR0dq0aZPDfGd6AQAAN68KEao2b96s9957T02aNHGYPnToUC1cuFDz5s3TmjVrdOTIET322GPm/Pz8fMXHxysvL08bNmzQrFmzlJycrJEjR5o1Bw4cUHx8vO6//35t375dQ4YM0bPPPqvly5ebNXPmzNGwYcM0atQobd26VU2bNlVcXJyOHTvmdC8AAODmZjMMwyjPBk6fPq3mzZvr3Xff1d///nc1a9ZMkydPVnZ2toKDgzV79mw9/vjjkqS9e/eqQYMGSktLU+vWrbV06VI9/PDDOnLkiEJDQyVJM2bM0PDhw3X8+HF5enpq+PDhWrx4sXbu3Glus3v37srKytKyZcskSdHR0WrZsqWmTp0qSbLb7YqIiNCgQYM0YsQIp3pxRk5OjgICApSdnS1/f3/LHkMrRY5YbNm6Do6Pt2xdAACUF2ffv8t9pCopKUnx8fGKjY11mL5lyxZduHDBYXr9+vVVs2ZNpaWlSZLS0tLUuHFjM1BJUlxcnHJycrRr1y6z5vJ1x8XFmevIy8vTli1bHGrc3NwUGxtr1jjTS1Fyc3OVk5PjcAMAADemSuW58U8//VRbt27V5s2bC83LyMiQp6enAgMDHaaHhoYqIyPDrLk0UBXML5h3pZqcnBydO3dOJ0+eVH5+fpE1e/fudbqXoowbN05jxowpdj4AALhxlNtI1eHDh/XCCy/o448/lre3d3m1cU29/PLLys7ONm+HDx8u75YAAMA1Um6hasuWLTp27JiaN2+uSpUqqVKlSlqzZo3efvttVapUSaGhocrLy1NWVpbDcpmZmQoLC5MkhYWFFfoGXsH9kmr8/f3l4+OjatWqyd3dvciaS9dRUi9F8fLykr+/v8MNAADcmMotVLVv3147duzQ9u3bzVuLFi3Us2dP898eHh5KTU01l0lPT9ehQ4cUExMjSYqJidGOHTscvqWXkpIif39/NWzY0Ky5dB0FNQXr8PT0VFRUlEON3W5XamqqWRMVFVViLwAA4OZWbudUValSRY0aNXKY5uvrq6pVq5rT+/Tpo2HDhikoKEj+/v4aNGiQYmJizG/bdejQQQ0bNlSvXr00YcIEZWRk6NVXX1VSUpK8vLwkSf3799fUqVP10ksv6ZlnntHKlSs1d+5cLV78f99yGzZsmBISEtSiRQu1atVKkydP1pkzZ9S7d29JUkBAQIm9AACAm1u5nqhekkmTJsnNzU1du3ZVbm6u4uLi9O6775rz3d3dtWjRIg0YMEAxMTHy9fVVQkKCxo4da9bUrl1bixcv1tChQzVlyhTVqFFD//znPxUXF2fWdOvWTcePH9fIkSOVkZGhZs2aadmyZQ4nr5fUCwAAuLmV+3WqbiZcpwoAANfjMtepAgAAuBEQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsUKm8G8CNK3LE4hJrDo6Pvw6dAABw7TFSBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABggXINVdOnT1eTJk3k7+8vf39/xcTEaOnSpeb88+fPKykpSVWrVpWfn5+6du2qzMxMh3UcOnRI8fHxqly5skJCQvTiiy/q4sWLDjWrV69W8+bN5eXlpdtvv13JycmFepk2bZoiIyPl7e2t6Ohobdq0yWG+M70AAICbV7mGqho1amj8+PHasmWLvv32Wz3wwAPq3Lmzdu3aJUkaOnSoFi5cqHnz5mnNmjU6cuSIHnvsMXP5/Px8xcfHKy8vTxs2bNCsWbOUnJyskSNHmjUHDhxQfHy87r//fm3fvl1DhgzRs88+q+XLl5s1c+bM0bBhwzRq1Cht3bpVTZs2VVxcnI4dO2bWlNQLAAC4udkMwzDKu4lLBQUFaeLEiXr88ccVHBys2bNn6/HHH5ck7d27Vw0aNFBaWppat26tpUuX6uGHH9aRI0cUGhoqSZoxY4aGDx+u48ePy9PTU8OHD9fixYu1c+dOcxvdu3dXVlaWli1bJkmKjo5Wy5YtNXXqVEmS3W5XRESEBg0apBEjRig7O7vEXpyRk5OjgIAAZWdny9/f37LHzEqRIxZf1+0dHB9/XbcHAEBpOfv+XWHOqcrPz9enn36qM2fOKCYmRlu2bNGFCxcUGxtr1tSvX181a9ZUWlqaJCktLU2NGzc2A5UkxcXFKScnxxztSktLc1hHQU3BOvLy8rRlyxaHGjc3N8XGxpo1zvRSlNzcXOXk5DjcAADAjancQ9WOHTvk5+cnLy8v9e/fX/Pnz1fDhg2VkZEhT09PBQYGOtSHhoYqIyNDkpSRkeEQqArmF8y7Uk1OTo7OnTun3377Tfn5+UXWXLqOknopyrhx4xQQEGDeIiIinHtQAACAyyn3UFWvXj1t375dGzdu1IABA5SQkKDdu3eXd1uWePnll5WdnW3eDh8+XN4tAQCAa6RSeTfg6emp22+/XZIUFRWlzZs3a8qUKerWrZvy8vKUlZXlMEKUmZmpsLAwSVJYWFihb+kVfCPv0prLv6WXmZkpf39/+fj4yN3dXe7u7kXWXLqOknopipeXl7y8vErxaAAAAFdV7iNVl7Pb7crNzVVUVJQ8PDyUmppqzktPT9ehQ4cUExMjSYqJidGOHTscvqWXkpIif39/NWzY0Ky5dB0FNQXr8PT0VFRUlEON3W5XamqqWeNMLwAA4OZWriNVL7/8sjp27KiaNWvq1KlTmj17tlavXq3ly5crICBAffr00bBhwxQUFCR/f38NGjRIMTEx5rftOnTooIYNG6pXr16aMGGCMjIy9OqrryopKckcIerfv7+mTp2ql156Sc8884xWrlypuXPnavHi//uW27Bhw5SQkKAWLVqoVatWmjx5ss6cOaPevXtLklO9AACAm1u5hqpjx47p6aef1tGjRxUQEKAmTZpo+fLl+tOf/iRJmjRpktzc3NS1a1fl5uYqLi5O7777rrm8u7u7Fi1apAEDBigmJka+vr5KSEjQ2LFjzZratWtr8eLFGjp0qKZMmaIaNWron//8p+Li4syabt266fjx4xo5cqQyMjLUrFkzLVu2zOHk9ZJ6AQAAN7cKd52qGxnXqSqM61QBACo6l7tOFQAAgCsjVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABaoVN4N4OYWOWJxiTUHx8dfh04AALg6jFQBAABYgFAFAABggTKFqttuu00nTpwoND0rK0u33XbbVTcFAADgasoUqg4ePKj8/PxC03Nzc/Xrr79edVMAAACuplQnqn/55Zfmv5cvX66AgADzfn5+vlJTUxUZGWlZcwAAAK6iVKGqS5cukiSbzaaEhASHeR4eHoqMjNSbb75pWXMAAACuolShym63S5Jq166tzZs3q1q1atekKQAAAFdTputUHThwwOo+AAAAXFqZL/6Zmpqq1NRUHTt2zBzBKvDhhx9edWMAAACupEyhasyYMRo7dqxatGih6tWry2azWd0XAACASylTqJoxY4aSk5PVq1cvq/sBAABwSWW6TlVeXp7atGljdS8AAAAuq0yh6tlnn9Xs2bOt7gUAAMBllenjv/Pnz+v999/XihUr1KRJE3l4eDjMf+uttyxpDgAAwFWUKVR9//33atasmSRp586dDvM4aR0AANyMyhSqVq1aZXUfAAAALq1M51QBAADAUZlGqu6///4rfsy3cuXKMjcEAADgisoUqgrOpypw4cIFbd++XTt37iz0Q8sAAAA3gzKFqkmTJhU5ffTo0Tp9+vRVNQQAAOCKLD2n6qmnnuJ3/wAAwE3J0lCVlpYmb29vK1cJAADgEsr08d9jjz3mcN8wDB09elTffvutXnvtNUsaAwAAcCVlClUBAQEO993c3FSvXj2NHTtWHTp0sKQxAAAAV1KmUDVz5kyr+wAAAHBpZQpVBbZs2aI9e/ZIku68807dddddljQFAADgasoUqo4dO6bu3btr9erVCgwMlCRlZWXp/vvv16effqrg4GArewQAAKjwyvTtv0GDBunUqVPatWuXfv/9d/3+++/auXOncnJyNHjwYKt7BAAAqPDKNFK1bNkyrVixQg0aNDCnNWzYUNOmTeNEdQAAcFMq00iV3W6Xh4dHoekeHh6y2+1X3RQAAICrKVOoeuCBB/TCCy/oyJEj5rRff/1VQ4cOVfv27S1rDgAAwFWUKVRNnTpVOTk5ioyMVJ06dVSnTh3Vrl1bOTk5euedd6zuEQAAoMIr0zlVERER2rp1q1asWKG9e/dKkho0aKDY2FhLmwMAAHAVpRqpWrlypRo2bKicnBzZbDb96U9/0qBBgzRo0CC1bNlSd955p9atW3etegUAAKiwShWqJk+erOeee07+/v6F5gUEBKhfv3566623LGsOAADAVZQqVH333Xd68MEHi53foUMHbdmy5aqbAgAAcDWlClWZmZlFXkqhQKVKlXT8+PGrbgoAAMDVlCpU3Xrrrdq5c2ex87///ntVr179qpsCAABwNaUKVQ899JBee+01nT9/vtC8c+fOadSoUXr44Yctaw4AAMBVlOqSCq+++qo+//xz1a1bVwMHDlS9evUkSXv37tW0adOUn5+vV1555Zo0CgAAUJGVKlSFhoZqw4YNGjBggF5++WUZhiFJstlsiouL07Rp0xQaGnpNGgUAAKjISn3xz1q1amnJkiU6efKkfvzxRxmGoTvuuEO33HLLtegPAADAJZTpiuqSdMstt6hly5ZW9oJrLHLE4vJuAQCAG1aZfvsPAAAAjghVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFigXEPVuHHj1LJlS1WpUkUhISHq0qWL0tPTHWrOnz+vpKQkVa1aVX5+furatasyMzMdag4dOqT4+HhVrlxZISEhevHFF3Xx4kWHmtWrV6t58+by8vLS7bffruTk5EL9TJs2TZGRkfL29lZ0dLQ2bdpU6l4AAMDNqVxD1Zo1a5SUlKRvvvlGKSkpunDhgjp06KAzZ86YNUOHDtXChQs1b948rVmzRkeOHNFjjz1mzs/Pz1d8fLzy8vK0YcMGzZo1S8nJyRo5cqRZc+DAAcXHx+v+++/X9u3bNWTIED377LNavny5WTNnzhwNGzZMo0aN0tatW9W0aVPFxcXp2LFjTvcCAABuXjaj4Af8KoDjx48rJCREa9as0b333qvs7GwFBwdr9uzZevzxxyX98ePNDRo0UFpamlq3bq2lS5fq4Ycf1pEjR8zfHZwxY4aGDx+u48ePy9PTU8OHD9fixYu1c+dOc1vdu3dXVlaWli1bJkmKjo5Wy5YtNXXqVEmS3W5XRESEBg0apBEjRjjVS0lycnIUEBCg7Oxs+fv7W/rYOcNVr6h+cHx8ebcAALiJOfv+XaHOqcrOzpYkBQUFSZK2bNmiCxcuKDY21qypX7++atasqbS0NElSWlqaGjdu7PBDznFxccrJydGuXbvMmkvXUVBTsI68vDxt2bLFocbNzU2xsbFmjTO9XC43N1c5OTkONwAAcGMq82//Wc1ut2vIkCG6++671ahRI0lSRkaGPD09FRgY6FAbGhqqjIwMs+bSQFUwv2DelWpycnJ07tw5nTx5Uvn5+UXW7N271+leLjdu3DiNGTPGyUcAxXFmhI3RLABAeaswI1VJSUnauXOnPv300/JuxTIvv/yysrOzzdvhw4fLuyUAAHCNVIiRqoEDB2rRokVau3atatSoYU4PCwtTXl6esrKyHEaIMjMzFRYWZtZc/i29gm/kXVpz+bf0MjMz5e/vLx8fH7m7u8vd3b3ImkvXUVIvl/Py8pKXl1cpHgkAAOCqynWkyjAMDRw4UPPnz9fKlStVu3Zth/lRUVHy8PBQamqqOS09PV2HDh1STEyMJCkmJkY7duxw+JZeSkqK/P391bBhQ7Pm0nUU1BSsw9PTU1FRUQ41drtdqampZo0zvQAAgJtXuY5UJSUlafbs2friiy9UpUoV89ykgIAA+fj4KCAgQH369NGwYcMUFBQkf39/DRo0SDExMea37Tp06KCGDRuqV69emjBhgjIyMvTqq68qKSnJHCXq37+/pk6dqpdeeknPPPOMVq5cqblz52rx4v87V2fYsGFKSEhQixYt1KpVK02ePFlnzpxR7969zZ5K6gUAANy8yjVUTZ8+XZJ03333OUyfOXOmEhMTJUmTJk2Sm5ubunbtqtzcXMXFxendd981a93d3bVo0SINGDBAMTEx8vX1VUJCgsaOHWvW1K5dW4sXL9bQoUM1ZcoU1ahRQ//85z8VFxdn1nTr1k3Hjx/XyJEjlZGRoWbNmmnZsmUOJ6+X1AsAALh5VajrVN3ouE7VtcO3/wAA14pLXqcKAADAVRGqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwQKXybgCwQuSIxSXWHBwffx06AQDcrBipAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxQrqFq7dq16tSpk8LDw2Wz2bRgwQKH+YZhaOTIkapevbp8fHwUGxurffv2OdT8/vvv6tmzp/z9/RUYGKg+ffro9OnTDjXff/+92rZtK29vb0VERGjChAmFepk3b57q168vb29vNW7cWEuWLCl1L6jYIkcsLvEGAEBZlWuoOnPmjJo2bapp06YVOX/ChAl6++23NWPGDG3cuFG+vr6Ki4vT+fPnzZqePXtq165dSklJ0aJFi7R27Vr17dvXnJ+Tk6MOHTqoVq1a2rJliyZOnKjRo0fr/fffN2s2bNigHj16qE+fPtq2bZu6dOmiLl26aOfOnaXqBQAA3LxshmEY5d2EJNlsNs2fP19dunSR9MfIUHh4uP7yl7/or3/9qyQpOztboaGhSk5OVvfu3bVnzx41bNhQmzdvVosWLSRJy5Yt00MPPaRffvlF4eHhmj59ul555RVlZGTI09NTkjRixAgtWLBAe/fulSR169ZNZ86c0aJFi8x+WrdurWbNmmnGjBlO9eKMnJwcBQQEKDs7W/7+/pY8bqXBSEzJDo6PL+8WAAAVjLPv3xX2nKoDBw4oIyNDsbGx5rSAgABFR0crLS1NkpSWlqbAwEAzUElSbGys3NzctHHjRrPm3nvvNQOVJMXFxSk9PV0nT540ay7dTkFNwXac6aUoubm5ysnJcbgBAIAbU4UNVRkZGZKk0NBQh+mhoaHmvIyMDIWEhDjMr1SpkoKCghxqilrHpdsorubS+SX1UpRx48YpICDAvEVERJSw1wAAwFVV2FB1I3j55ZeVnZ1t3g4fPlzeLQEAgGukwoaqsLAwSVJmZqbD9MzMTHNeWFiYjh075jD/4sWL+v333x1qilrHpdsorubS+SX1UhQvLy/5+/s73AAAwI2pwoaq2rVrKywsTKmpqea0nJwcbdy4UTExMZKkmJgYZWVlacuWLWbNypUrZbfbFR0dbdasXbtWFy5cMGtSUlJUr1493XLLLWbNpdspqCnYjjO9AACAm1u5hqrTp09r+/bt2r59u6Q/Tgjfvn27Dh06JJvNpiFDhujvf/+7vvzyS+3YsUNPP/20wsPDzW8INmjQQA8++KCee+45bdq0SevXr9fAgQPVvXt3hYeHS5L+/Oc/y9PTU3369NGuXbs0Z84cTZkyRcOGDTP7eOGFF7Rs2TK9+eab2rt3r0aPHq1vv/1WAwcOlCSnegEAADe3SuW58W+//Vb333+/eb8g6CQkJCg5OVkvvfSSzpw5o759+yorK0v33HOPli1bJm9vb3OZjz/+WAMHDlT79u3l5uamrl276u233zbnBwQE6KuvvlJSUpKioqJUrVo1jRw50uFaVm3atNHs2bP16quv6m9/+5vuuOMOLViwQI0aNTJrnOkFAADcvCrMdapuBlynquLjOlUAgMu5/HWqAAAAXAmhCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsUK5XVAcqGmcukMoFQgEARWGkCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMACfPvvBuHMt9YAAMC1w0gVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiA61QBpeTMNcEOjo+/Dp0AACoSRqoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACzAFdWBa4CrrgPAzYeRKgAAAAsQqgAAACxAqAIAALAA51QB5YTzrgDgxsJIFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAF+PYfUIHxDUEAcB2MVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAW4ER1wMVxMjsAVAyMVAEAAFiAkSrgJsBoFgBce4xUAQAAWICRKgCSGM0CgKvFSBUAAIAFGKkC4DRXHM1ypmerVLR9B3B9EaoAWOp6Bq/rGZgAoCSEKgAVEoEJgKshVAG47m7UwGTVfvExIq4Gz8PyQ6gCAMBF3Kj/IblREKoAoIJxxS8E4MbD87D0CFUA4IJ4wwMqHkIVANygCF6uxRU/2uM55oiLfwIAAFiAkSoAuIkx0nB9uOIolFVupucYoQoAAJSrGyV4EapKadq0aZo4caIyMjLUtGlTvfPOO2rVqlV5twUA18zNfN2jm3mECaVHqCqFOXPmaNiwYZoxY4aio6M1efJkxcXFKT09XSEhIeXdHgBUaAQU3OhshmEY5d2Eq4iOjlbLli01depUSZLdbldERIQGDRqkESNGlLh8Tk6OAgIClJ2dLX9/f0t7448VAOBmd61GQ519/+bbf07Ky8vTli1bFBsba05zc3NTbGys0tLSyrEzAABQEfDxn5N+++035efnKzQ01GF6aGio9u7dW+Qyubm5ys3NNe9nZ2dL+iPxWs2ee9bydQIA4Equxfvrpest6cM9QtU1NG7cOI0ZM6bQ9IiIiHLoBgCAG1vA5Gu7/lOnTikgIKDY+YQqJ1WrVk3u7u7KzMx0mJ6ZmamwsLAil3n55Zc1bNgw877dbtfvv/+uqlWrymazXdN+c3JyFBERocOHD1t+/lZ5Y99c1428f+yba7qR9026sffveu6bYRg6deqUwsPDr1hHqHKSp6enoqKilJqaqi5dukj6IySlpqZq4MCBRS7j5eUlLy8vh2mBgYHXuFNH/v7+N9wLqQD75rpu5P1j31zTjbxv0o29f9dr3640QlWAUFUKw4YNU0JCglq0aKFWrVpp8uTJOnPmjHr37l3erQEAgHJGqCqFbt266fjx4xo5cqQyMjLUrFkzLVu2rNDJ6wAA4OZDqCqlgQMHFvtxX0Xi5eWlUaNGFfr48UbAvrmuG3n/2DfXdCPvm3Rj719F3Dcu/gkAAGABLv4JAABgAUIVAACABQhVAAAAFiBUAQAAWIBQ5cKmTZumyMhIeXt7Kzo6Wps2bbpi/bx581S/fn15e3urcePGWrJkyXXq1Hnjxo1Ty5YtVaVKFYWEhKhLly5KT0+/4jLJycmy2WwON29v7+vUcemMHj26UK/169e/4jKucNwkKTIystC+2Ww2JSUlFVlfkY/b2rVr1alTJ4WHh8tms2nBggUO8w3D0MiRI1W9enX5+PgoNjZW+/btK3G9pX3NXgtX2rcLFy5o+PDhaty4sXx9fRUeHq6nn35aR44cueI6y/K8vlZKOnaJiYmFen3wwQdLXG9FP3aSinz92Ww2TZw4sdh1VpRj58zf/vPnzyspKUlVq1aVn5+funbtWuhXTi5X1tdqWRGqXNScOXM0bNgwjRo1Slu3blXTpk0VFxenY8eOFVm/YcMG9ejRQ3369NG2bdvUpUsXdenSRTt37rzOnV/ZmjVrlJSUpG+++UYpKSm6cOGCOnTooDNnzlxxOX9/fx09etS8/fzzz9ep49K78847HXr9+uuvi611leMmSZs3b3bYr5SUFEnSE088UewyFfW4nTlzRk2bNtW0adOKnD9hwgS9/fbbmjFjhjZu3ChfX1/FxcXp/Pnzxa6ztK/Za+VK+3b27Flt3bpVr732mrZu3arPP/9c6enpeuSRR0pcb2me19dSScdOkh588EGHXj/55JMrrtMVjp0kh306evSoPvzwQ9lsNnXt2vWK660Ix86Zv/1Dhw7VwoULNW/ePK1Zs0ZHjhzRY489dsX1luW1elUMuKRWrVoZSUlJ5v38/HwjPDzcGDduXJH1Tz75pBEfH+8wLTo62ujXr9817fNqHTt2zJBkrFmzptiamTNnGgEBAdevqaswatQoo2nTpk7Xu+pxMwzDeOGFF4w6deoYdru9yPmuctwkGfPnzzfv2+12IywszJg4caI5LSsry/Dy8jI++eSTYtdT2tfs9XD5vhVl06ZNhiTj559/LramtM/r66Wo/UtISDA6d+5cqvW46rHr3Lmz8cADD1yxpqIeu8v/9mdlZRkeHh7GvHnzzJo9e/YYkoy0tLQi11HW1+rVYKTKBeXl5WnLli2KjY01p7m5uSk2NlZpaWlFLpOWluZQL0lxcXHF1lcU2dnZkqSgoKAr1p0+fVq1atVSRESEOnfurF27dl2P9spk3759Cg8P12233aaePXvq0KFDxda66nHLy8vTRx99pGeeeeaKPx7uSsetwIEDB5SRkeFwXAICAhQdHV3scSnLa7aiyM7Ols1mK/F3S0vzvC5vq1evVkhIiOrVq6cBAwboxIkTxda66rHLzMzU4sWL1adPnxJrK+Kxu/xv/5YtW3ThwgWH41C/fn3VrFmz2ONQltfq1SJUuaDffvtN+fn5hX4eJzQ0VBkZGUUuk5GRUar6isBut2vIkCG6++671ahRo2Lr6tWrpw8//FBffPGFPvroI9ntdrVp00a//PLLdezWOdHR0UpOTtayZcs0ffp0HThwQG3bttWpU6eKrHfF4yZJCxYsUFZWlhITE4utcaXjdqmCx740x6Usr9mK4Pz58xo+fLh69OhxxR+sLe3zujw9+OCD+ve//63U1FS98cYbWrNmjTp27Kj8/Pwi61312M2aNUtVqlQp8eOxinjsivrbn5GRIU9Pz0LhvqT3vYIaZ5e5WvxMDSqspKQk7dy5s8TP92NiYhQTE2Peb9OmjRo0aKD33ntPr7/++rVus1Q6duxo/rtJkyaKjo5WrVq1NHfuXKf+R+kq/vWvf6ljx44KDw8vtsaVjtvN6MKFC3ryySdlGIamT59+xVpXel53797d/Hfjxo3VpEkT1alTR6tXr1b79u3LsTNrffjhh+rZs2eJX/6oiMfO2b/9FREjVS6oWrVqcnd3L/Sth8zMTIWFhRW5TFhYWKnqy9vAgQO1aNEirVq1SjVq1CjVsh4eHrrrrrv0448/XqPurBMYGKi6desW26urHTdJ+vnnn7VixQo9++yzpVrOVY5bwWNfmuNSltdseSoIVD///LNSUlKuOEpVlJKe1xXJbbfdpmrVqhXbq6sdO0lat26d0tPTS/0alMr/2BX3tz8sLEx5eXnKyspyqC/pfa+gxtllrhahygV5enoqKipKqamp5jS73a7U1FSH//lfKiYmxqFeklJSUoqtLy+GYWjgwIGaP3++Vq5cqdq1a5d6Hfn5+dqxY4eqV69+DTq01unTp7V///5ie3WV43apmTNnKiQkRPHx8aVazlWOW+3atRUWFuZwXHJycrRx48Zij0tZXrPlpSBQ7du3TytWrFDVqlVLvY6SntcVyS+//KITJ04U26srHbsC//rXvxQVFaWmTZuWetnyOnYl/e2PioqSh4eHw3FIT0/XoUOHij0OZXmtWrEjcEGffvqp4eXlZSQnJxu7d+82+vbtawQGBhoZGRmGYRhGr169jBEjRpj169evNypVqmT8z//8j7Fnzx5j1KhRhoeHh7Fjx47y2oUiDRgwwAgICDBWr15tHD161LydPXvWrLl838aMGWMsX77c2L9/v7Flyxaje/fuhre3t7Fr167y2IUr+stf/mKsXr3aOHDggLF+/XojNjbWqFatmnHs2DHDMFz3uBXIz883atasaQwfPrzQPFc6bqdOnTK2bdtmbNu2zZBkvPXWW8a2bdvMb8CNHz/eCAwMNL744gvj+++/Nzp37mzUrl3bOHfunLmOBx54wHjnnXfM+yW9ZivCvuXl5RmPPPKIUaNGDWP79u0Or8Hc3Nxi962k53VF2b9Tp04Zf/3rX420tDTjwIEDxooVK4zmzZsbd9xxh3H+/Pli988Vjl2B7Oxso3Llysb06dOLXEdFPXbO/O3v37+/UbNmTWPlypXGt99+a8TExBgxMTEO66lXr57x+eefm/edea1aiVDlwt555x2jZs2ahqenp9GqVSvjm2++Mee1a9fOSEhIcKifO3euUbduXcPT09O48847jcWLF1/njksmqcjbzJkzzZrL923IkCHm4xAaGmo89NBDxtatW69/807o1q2bUb16dcPT09O49dZbjW7duhk//vijOd9Vj1uB5cuXG5KM9PT0QvNc6bitWrWqyOdhQf92u9147bXXjNDQUMPLy8to3759oX2uVauWMWrUKIdpV3rNXi9X2rcDBw4U+xpctWqVuY7L962k5/X1dKX9O3v2rNGhQwcjODjY8PDwMGrVqmU899xzhcKRKx67Au+9957h4+NjZGVlFbmOinrsnPnbf+7cOeP55583brnlFqNy5crGo48+ahw9erTQei5dxpnXqpVs/9sEAAAArgLnVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAFze6NGj1axZs2u6jfvuu09Dhgwx70dGRmry5MnXdJsAXAuhCkCFdXmQKc5f//rXQr+ReK1t3rxZffv2daqWAAbcHCqVdwMAUFaGYSg/P19+fn7y8/O7rtsODg6+rtsDUPExUgWgQkpMTNSaNWs0ZcoU2Ww22Ww2JScny2azaenSpYqKipKXl5e+/vrrQh//JSYmqkuXLhozZoyCg4Pl7++v/v37Ky8vz6ltnzlzRk8//bT8/PxUvXp1vfnmm4VqLh19MgxDo0ePVs2aNeXl5aXw8HANHjxY0h+jbT///LOGDh1q7ocknThxQj169NCtt96qypUrq3Hjxvrkk08ctnHfffdp8ODBeumllxQUFKSwsDCNHj3aoSYrK0v9+vVTaGiovL291ahRIy1atMic//XXX6tt27by8fFRRESEBg8erDNnzjj1OAAoHUIVgAppypQpiomJ0XPPPaejR4/q6NGjioiIkCSNGDFC48eP1549e9SkSZMil09NTdWePXu0evVqffLJJ/r88881ZswYp7b94osvas2aNfriiy/01VdfafXq1dq6dWux9Z999pkmTZqk9957T/v27dOCBQvUuHFjSdLnn3+uGjVqaOzYseZ+SNL58+cVFRWlxYsXa+fOnerbt6969eqlTZs2Oax71qxZ8vX11caNGzVhwgSNHTtWKSkpkiS73a6OHTtq/fr1+uijj7R7926NHz9e7u7ukqT9+/frwQcfVNeuXfX9999rzpw5+vrrrzVw4ECnHgcApXTNfqoZAK5Su3btjBdeeMG8v2rVKkOSsWDBAoe6UaNGGU2bNjXvJyQkGEFBQcaZM2fMadOnTzf8/PyM/Pz8K27z1KlThqenpzF37lxz2okTJwwfHx+HXmrVqmVMmjTJMAzDePPNN426desaeXl5Ra7z0toriY+PN/7yl7+Y99u1a2fcc889DjUtW7Y0hg8fbhiGYSxfvtxwc3Mz0tPTi1xfnz59jL59+zpMW7duneHm5macO3euxH4AlA4jVQBcTosWLUqsadq0qSpXrmzej4mJ0enTp3X48OErLrd//37l5eUpOjranBYUFKR69eoVu8wTTzyhc+fO6bbbbtNzzz2n+fPn6+LFi1fcTn5+vl5//XU1btxYQUFB8vPz0/Lly3Xo0CGHustH4qpXr65jx45JkrZv364aNWqobt26RW7ju+++U3JysnnOmZ+fn+Li4mS323XgwIEr9geg9DhRHYDL8fX1Le8WHERERCg9PV0rVqxQSkqKnn/+eU2cOFFr1qyRh4dHkctMnDhRU6ZM0eTJk9W4cWP5+vpqyJAhhc77unx5m80mu90uSfLx8bliX6dPn1a/fv3M87suVbNmzdLsIgAnEKoAVFienp7Kz88v07Lfffedzp07ZwaPb775Rn5+fuZ5WcWpU6eOPDw8tHHjRjN4nDx5Uj/88IPatWtX7HI+Pj7q1KmTOnXqpKSkJNWvX187duxQ8+bNi9yP9evXq3Pnznrqqack/XF+1A8//KCGDRs6vY9NmjTRL7/8oh9++KHI0armzZtr9+7duv32251eJ4Cy4+M/ABVWZGSkNm7cqIMHD+q3334zR2ickZeXpz59+mj37t1asmSJRo0apYEDB8rN7cp/9vz8/NSnTx+9+OKLWrlypXbu3KnExMQrLpecnKx//etf2rlzp3766Sd99NFH8vHxUa1atcz9WLt2rX799Vf99ttvkqQ77rhDKSkp2rBhg/bs2aN+/fopMzPT6f2TpHbt2unee+9V165dlZKSogMHDmjp0qVatmyZJGn48OHasGGDBg4cqO3bt2vfvn364osvOFEduEYIVQAqrL/+9a9yd3dXw4YNFRwcXOh8oytp37697rjjDt17773q1q2bHnnkkUKXIyjOxIkT1bZtW3Xq1EmxsbG65557FBUVVWx9YGCgPvjgA919991q0qSJVqxYoYULF6pq1aqSpLFjx+rgwYOqU6eOeX2rV199Vc2bN1dcXJzuu+8+hYWFqUuXLk7vX4HPPvtMLVu2VI8ePdSwYUO99NJL5qhYkyZNtGbNGv3www9q27at7rrrLo0cOVLh4eGl3g6AktkMwzDKuwkAsFJiYqKysrK0YMGC8m4FwE2EkSoAAAALEKoA3FQOHTrkcImBy2+l+YgRAC7Fx38AbioXL17UwYMHi50fGRmpSpX4YjSA0iNUAQAAWICP/wAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAAC/x/mDryZMWdUXYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}